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      </a></div> <span class="tag-space" data-v-a5c9dc12>/</span> <div class="inblock tag-list" data-v-a5c9dc12><a href="/tag/attention/" class="tag-text" data-v-a5c9dc12>attention
      </a></div></div> <div class="content content__default" data-v-a5c9dc12><h3 id="motivation"><a href="#motivation" class="header-anchor">#</a> motivation</h3> <ul><li>事件是异步产生的，事件发生的时间戳与时间序列不同，异步的事件产生的时间戳能够反映网络动力学</li> <li>而时间序列数据能够反映背景环境的周期性更新，例如计算服务器的温度、病人的血压等</li> <li>最近的一些研究有对连续时间点过程进行建模的<sup class="footnote-ref"><a href="#fn1" id="fnref1">[1]</a></sup>、<sup class="footnote-ref"><a href="#fn2" id="fnref2">[2]</a></sup>、<sup class="footnote-ref"><a href="#fn3" id="fnref3">[3]</a></sup>、<sup class="footnote-ref"><a href="#fn4" id="fnref4">[4]</a></sup>、<sup class="footnote-ref"><a href="#fn5" id="fnref5">[5]</a></sup>，以及对时间序列建模的<sup class="footnote-ref"><a href="#fn6" id="fnref6">[6]</a></sup>、<sup class="footnote-ref"><a href="#fn7" id="fnref7">[7]</a></sup>、<sup class="footnote-ref"><a href="#fn8" id="fnref8">[8]</a></sup>，但是大部分都是将这两种进行单独处理</li></ul> <h3 id="参考文献"><a href="#参考文献" class="header-anchor">#</a> 参考文献</h3> <hr class="footnotes-sep"> <section class="footnotes"><ol class="footnotes-list"><li id="fn1" class="footnote-item"><p>L. Li, H. Deng, A. Dong, Y. Chang, and H. Zha, “Identifying and labeling search tasks via query-based hawkes processes,” in KDD, 2014. <a href="#fnref1" class="footnote-backref">↩︎</a></p></li> <li id="fn2" class="footnote-item"><p>L. Yu, P. Cui, F. Wang, C. Song, and S. Yang, “From micro to macro: Uncovering and predicting information cascading process with behavioral dynamics,” 2015. <a href="#fnref2" class="footnote-backref">↩︎</a></p></li> <li id="fn3" class="footnote-item"><p>M. Farajtabar, Y. Wang, M. G. Rodriguez, S. Li, H. Zha, and L. Song, “Coevolve: A joint point process model for information diffusion and network co-evolution,” in Advances in Neural Information Processing Systems, 2015, pp. 1954–1962. <a href="#fnref3" class="footnote-backref">↩︎</a></p></li> <li id="fn4" class="footnote-item"><p>N. Du, H. Dai, R. Trivedi, U. Upadhyay, M. Gomez-Rodriguez, and L. Song, “Recurrent marked temporal point processes: Embedding event history to vectore,” in KDD, 2016. <a href="#fnref4" class="footnote-backref">↩︎</a></p></li> <li id="fn5" class="footnote-item"><p>M. Farajtabar, N. Du, M. G. Rodriguez, I. Valera, H. Zha, and L. Song, “Shaping social activity by incentivizing users,” in Advances in neural information processing systems, 2014, pp. 2474–2482. <a href="#fnref5" class="footnote-backref">↩︎</a></p></li> <li id="fn6" class="footnote-item"><p>G. E. Box, G. M. Jenkins, G. C. Reinsel, and G. M. Ljung, Time series analysis: forecasting and control. John Wiley &amp; Sons, 2015. <a href="#fnref6" class="footnote-backref">↩︎</a></p></li> <li id="fn7" class="footnote-item"><p>C. Chatfield, The analysis of time series: an introduction. CRC press, 2016. <a href="#fnref7" class="footnote-backref">↩︎</a></p></li> <li id="fn8" class="footnote-item"><p>V. Guralnik and J. Srivastava, “Event detection from time series data,” in Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 1999, pp. 33–42 <a href="#fnref8" class="footnote-backref">↩︎</a></p></li></ol></section></div> <div class="content-time" data-v-a5c9dc12><time datetime="2018年11月30日" class="time-text" data-v-a5c9dc12>Create Time: 2018年11月30日
    </time> <time datetime="2020年9月13日" class="time-text" data-v-a5c9dc12>Last Updated: 2020年9月13日
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